Classification of ambiguous geographic references

ABSTRACT

A location classifier generates location information based on textual strings in input text. The location information defines potential geographical relevance of the input text. In determining the location information, the location classifier may receive at least one geo-relevance profile associated with at least one string in the input text, obtain a combined geo-relevance profile for the document from the at least one geo-relevance profile, and determine geographical relevance of the input text based on the combined geo-relevance profile.

BACKGROUND

A. Field of the Invention

Systems and methods described herein relate to search engines and, moreparticularly, to techniques for classifying text as relevant togeographic regions.

B. Description of Related Art

The World Wide Web (“web”) contains a vast amount of information.Locating a desired portion of the information, however, can bechallenging. This problem is compounded because the amount ofinformation on the web and the number of new users inexperienced at websearching are growing rapidly.

Search engines attempt to return hyperlinks to web pages in which a useris interested. Generally, search engines base their determination of theuser's interest on search terms (called a search query) entered by theuser. The goal of the search engine is to provide links to high quality,relevant results (e.g., web pages) to the user based on the searchquery. Typically, the search engine accomplishes this by matching theterms in the search query to a corpus of pre-stored web pages. Web pagesthat contain the user's search terms are “hits” and are returned to theuser as links.

In an attempt to increase the relevancy and quality of the web pagesreturned to the user, a search engine may attempt to sort the list ofhits so that the most relevant and/or highest quality pages are at thetop of the list of hits returned to the user. For example, the searchengine may assign a rank or score to each hit, where the score isdesigned to correspond to the relevance or importance of the web page.

Local search engines are search engines that attempt to return relevantweb pages within a specific geographic region. When indexing documentsfor a local search engine, it is desirable to be able to, whenappropriate, automatically associate documents, or sections ofdocuments, with specific geographic regions. For example, a web pageabout a restaurant in New York City should be associated with New YorkCity. In many cases, geographically specific web pages include postaladdresses or other geographic information that unambiguously associatesthe web page with the geographic region. In other cases, however, theweb page may be related to a specific geographic region but yet mayinclude only partial postal address information or include other termsthat may not be easily recognized as being associated with a specificgeographic location. This makes it difficult to determine the geographicregion with which the web page is associated.

SUMMARY OF THE INVENTION

One aspect of the invention is directed to a method of determininggeographical relevance of a document. The method includes receiving atleast one geo-relevance profile associated with at least one string inthe document, obtaining a combined geo-relevance profile for thedocument from the at least one geo-relevance profile, and determininggeographical relevance of the document based on the combinedgeo-relevance profile.

Another aspect of the invention is directed to a computer-readablemedium that contains programming instructions for execution by aprocessor. The computer-readable medium includes programminginstructions for receiving geo-relevance profiles associated withrespective strings in a document, the geo-relevance profiles eachdefining the geographical relevance of the string with respect togeographical regions. The computer-readable medium further includesprogramming instructions for determining geographical relevance of thedocument based on the geo-relevance profiles.

Yet another aspect of the invention is directed to a method forgenerating a geo-relevance profile for a string. The method includesdetermining a plurality of sections of training text in which eachsection of training text is associated with a geographical region,accumulating occurrences of the string in the plurality of selections oftraining text, and generating the geo-relevance profile as a histogrambased on the accumulated occurrences of the string.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate an embodiment of the inventionand, together with the description, explain the invention. In thedrawings,

FIG. 1 is a diagram illustrating general concepts consistent withaspects of the invention;

FIG. 2 is an exemplary diagram of a network in which systems and methodsconsistent with the principles of the invention may be implemented;

FIG. 3 is an exemplary diagram of a client or server shown in FIG. 2;

FIG. 4 is a flow chart illustrating an exemplary procedure for trainingthe location classifier engine shown in FIGS. 1 and 2;

FIG. 5 is a diagram illustrating an exemplary document in which twogeographic signals are present;

FIG. 6 is a diagram of a portion of an exemplary table illustratingtraining data;

FIGS. 7A-7C are diagrams illustrating exemplary geo-relevance profilesfor terms/phrases;

FIG. 8 is a diagram conceptually illustrating a table includingexemplary terms/phrases and their corresponding geo-relevance profiles;

FIG. 9 is a flow chart illustrating exemplary operation of the locationclassifier in determining potentially relevant geographical areas forinput documents;

FIGS. 10A-10C illustrate combining multiple geo-relevance profiles toobtain a combined profile; and

FIG. 11 is a diagram illustrating an exemplary implementation of thelocation classifier implemented in the context of a search engine.

DETAILED DESCRIPTION

The following detailed description of the invention refers to theaccompanying drawings. The detailed description does not limit theinvention.

Overview

A location classifier is described herein that automatically classifiesinput text, when appropriate, to specific geographic regions(s). FIG. 1is a diagram illustrating general concepts consistent with aspects ofthe invention, including a location classifier 100. As an example of theoperation of location classifier 100, consider an input document, suchas the exemplary document shown in FIG. 1, describing a business onCastro Street in Mountain View, Calif. Assume that the documentdescribes the business as being on Castro Street in the bay area, butdoes not specifically include a full postal address, telephone number,and never explicitly states “Mountain View, Calif.”

Location classifier 100 may recognize that the bi-grams “bay area” and“Castro Street” in the document are geographically significant. “Bayarea,” by itself, is frequently used to refer to the area surroundingthe San Francisco bay in Calif., but it is also commonly used to referto other bay locations, such as the Green Bay area in Wis. Additionally,Castro Street, by itself, may be a common street name. Locationclassifier 100 may resolve the individual geographical ambiguity in “BayArea” and “Castro Street” by recognizing that the occurrence of both ofthese phrases is likely to indicate that the document pertains to theCastro Street located in Mountain View, Calif.

Location classifier 100 may then generate a complete address or otherlocation identifier, such as Mountain View, Calif., 94043, aspotentially corresponding to the business mentioned in the document.

Exemplary Network Overview

FIG. 2 is an exemplary diagram of a network 200 in which systems andmethods consistent with the principles of the invention may beimplemented. Network 200 may include clients 210 connected to a server220 via a network 240. Network 240 may include a local area network(LAN), a wide area network (WAN), a telephone network, such as thePublic Switched Telephone Network (PSTN), an intranet, the Internet, ora combination of networks. Two clients 210 and one server 220 have beenillustrated as connected to network 240 for simplicity. In practice,there may be more clients and/or servers. Also, in some instances, aclient may perform the functions of a server and a server may performthe functions of a client.

A client 210 may include a device, such as a wireless telephone, apersonal computer, a personal digital assistant (PDA), a lap top, oranother type of computation or communication device, a thread or processrunning on one of these devices, and/or an object executable by one ofthese devices. Server 220 may include a server device that processes,searches, and/or maintains documents. Clients 210 and server 220 mayconnect to network 240 via wired, wireless, or optical connections.

Server 220 may include a search engine 225 usable by clients 210. Searchengine 225 may be a search engine, such as a query-based document searchengine. In some implementations, search engine 225 may particularly bedesigned to return results local to geographic regions. Search engine225 may include location classifier 100. Location classifier 100receives input data that may include partial addresses or terms/phraseshaving geographic relevance and may generate one or more locationidentifiers corresponding to geographic areas that correspond to theinput documents. Location classifier 100 may, for instance, be used bysearch engine 225 to associate documents, such as web pages, withgeographic areas or to determine whether a user search query relates toa specific geographic location.

A document, as the term is used herein, is to be broadly interpreted toinclude any machine-readable and machine-storable work product. Adocument may be an e-mail, a search query, a file, a combination offiles, one or more files with embedded links to other files, a newsgroup posting, etc. In the context of the Internet, a common document isa web page. Web pages often include content and may include embeddedinformation (such as meta information, hyperlinks, etc.) and/or embeddedinstructions (such as JavaScript, etc.).

Exemplary Client/Server Architecture

FIG. 3 is an exemplary diagram of a client 210 or server 220, referredto as computing device 300, according to an implementation consistentwith the principles of the invention. Computing device 300 may include abus 310, a processor 320, a main memory 330, a read only memory (ROM)340, a storage device 350, an input device 360, an output device 370,and a communication interface 380. Bus 310 may include a path thatpermits communication among the components of computing device 300.

Processor 320 may include any type of conventional processor,microprocessor, or processing logic that may interpret and executeinstructions. Main memory 330 may include a random access memory (RAM)or another type of dynamic storage device that stores information andinstructions for execution by processor 320. ROM 340 may include aconventional ROM device or another type of static storage device thatstores static information and instructions for use by processor 320.Storage device 350 may include a magnetic and/or optical recordingmedium and its corresponding drive.

Input device 360 may include a conventional mechanism that permits auser to input information to computing device 300, such as a keyboard, amouse, a pen, voice recognition and/or biometric mechanisms, etc. Outputdevice 370 may include a conventional mechanism that outputs informationto the user, including a display, a printer, a speaker, etc.Communication interface 380 may include any transceiver-like mechanismthat enables computing device 300 to communicate with other devicesand/or systems. For example, communication interface 380 may includemechanisms for communicating with another device or system via anetwork, such as network 240.

Server 220, consistent with the principles of the invention, performscertain searching or document retrieval related operations throughsearch engine 225 and/or location classifier engine 100. Search engine225 and/or location classifier engine 100 may be stored in acomputer-readable medium, such as memory 330. A computer-readable mediummay be defined as one or more physical or logical memory devices and/orcarrier waves.

The software instructions defining search engine 225 may be read intomemory 330 from another computer-readable medium, such as data storagedevice 350, or from another device via communication interface 380. Thesoftware instructions contained in memory 330 cause processor 320 toperform processes that will be described later. Alternatively, hardwiredcircuitry may be used in place of or in combination with softwareinstructions to implement processes consistent with the presentinvention. Thus, implementations consistent with the principles of theinvention are not limited to any specific combination of hardwarecircuitry and software.

Training of Location Classifier 100

Location classifier 100 may automatically generate geographic locationinformation for an input document or section of a document. Beforelocation classifier 100 can generate the geographic locationinformation, it may be trained on a number of training documents. In oneimplementation, the documents may be web pages.

FIG. 4 is a flow chart illustrating exemplary procedures for traininglocation classifier 100.

Location classifier 100 may be trained on a large number of documents,such as a large number of web documents. Location classifier engine 100may begin training by retrieving a first of the documents, (act 401),and locating known geographic signals within the document (act 402). Aknown geographic signal may include, for example, a complete addressthat unambiguously specifies a geographic location. The geographicsignal can be located by, for example, pattern matching techniques thatlook for sections of text that are in the general form of an address.For example, location classifier engine 100 may look for zip codes asfive digit integers located near a state name or state abbreviation andstreet names as a series of numerals followed by a string that includesa word such as “street,” “st.,” “drive,” etc. In this manner, locationclassifier 100 may locate the known geographic signals as sections oftext that unambiguously reference geographic addresses.

FIG. 5 is a diagram illustrating an exemplary document 500 in which twogeographic signals are present. As shown, document 500 includes a firstgeographic signal 505, a paragraph of text 510, a second geographicsignal 515, and a second paragraph of text 520.

The first geographic signal, signal 505, is for a hypothetical coffeeshop called “Coffee Time” that specifies, as a standard postal address,the location of Coffee Time. Location classifier 100 may recognize thisaddress as a valid address based both on the structure of the addressand/or based on the fact that the zip code, street name, and city nameare all consistent with a known location in California. Similarly,location classifier 100 may recognize that geographic signal 515 alsorepresents a valid address that is unambiguously associated with aphysical location. One of ordinary skill in the art will recognize thatother techniques for determining whether a document is associated with ageographic location can be used, such as manual classification ofdocuments.

Documents that are determined to be associated with valid geographicsignals in act 402 are assumed to be documents that correspond to aknown geographic region(s). If the document currently being processed isnot such a document, such as a web document that is not associated witha particular geographic region, the next document may be processed (acts403 and 405). For documents that include valid geographic signals,however, location classifier 100 may select text from the document to beused as training text associated with the found geographic signal(s)(act 404).

The text selected in act 404 as the training text associated with thedocument may be selected in a number of different ways. For example, afixed window (e.g., a 100 term window) around each geographic signal maybe selected as the training text. In other implementations, the wholedocument may be selected. In still other implementations, documents withmultiple geographic signals may be segmented based on visual breaks inthe document and the training text taken from the segments. For thedocument shown in FIG. 5, for instance, paragraph 510 may be associatedwith address signal 505 and paragraph 520 may be associated with addresssignal 515.

Acts 402-405 may be repeated for each document in the corpus ofdocuments that are to be used as training documents (act 406). Ingeneral, acts 401-405 serve to generate training data in which each of anumber (usually a large number) of known locations are associated withtext. FIG. 6 is a diagram of a portion of a table illustrating exemplarytraining data generated in acts 402-405. Table 600 may include a numberof location identifier fields 605 and corresponding sections of text610. Identifier fields 605 may be based on the geographic signals andtext sections 610 may include the text selected for each geographicsignal. Thus, each located geographic signal may correspond to an entryin table 600.

In one implementation, location identifier fields 605 may include thezip codes corresponding to the geographic signals identified in act 402.Zip codes are particularly useful to use as an identifier for ageographic location because zip codes that are close to one anothernumerically tend to correspond to locations that are close to oneanother geographically. Location identifiers other than zip codes may,however, also be used.

Two entries are particularly shown in table 600. These two entriescorrespond to the two geographic signals from document 500. The firstentry includes the zip code 94040 as the located identifier andparagraph 510 as the selected text. The second entry includes the zipcode 94041 as the located identifier and paragraph 520 as the selectedtext.

Although the training data in table 600 is described herein as beinggenerated by location classifier 100 in a same process as the rest ofthe training (i.e., acts 407-410), the training data could be generatedahead of time or by another component or device.

Consistent with an aspect of the invention, location classifier 100operates, in part, on the premise that text in a document that is in thevicinity of a geographic signal is biased towards using terms or phrasesthat relate to the geographic signal. The training data obtained in acts401-406 may be further processed by location classifier 100, as will bedescribed below with reference to acts 407-410, to obtain geo-relevanceprofiles for certain terms/phrases.

Location classifier 100 may begin by accumulating, for a select term orphrase, all occurrences of the term/phrase (also referred to as atextual strings or just strings herein) in the text selections 610relative to the location identifiers for which the term/phrase occurs(act 407). In other words, location classifier 100 may generate ahistogram relating the number of occurrences of the term/phrase to thelocation identifiers. The histogram will also be referred to herein asthe geo-relevance profile of the term/phrase.

FIG. 7A is a diagram illustrating an exemplary histogram 700 for thebi-gram “capitol hill.” As shown, the histogram includes three dominantpeaks, a large peak centered in the vicinity of zip code 20515, whichcorresponds to the “Capitol Hill” area in Washington, D.C., a relativelysmall peak centered in the vicinity of zip code 95814, which correspondsto the “Capitol Hill” area in Sacramento, Calif., and a moderate peakcentered in the vicinity of zip code 98104, which corresponds to the“Capitol Hill” area in Seattle, Wash. Although text selections 610potentially included numerous references to “capitol hill,” many ofwhich were associated with areas not in the vicinity of Washington,D.C., Sacramento, or Seattle, histogram 700 illustrates that overall,“capitol hill” tends to be used when referring to one of these threelocations. Washington, D.C., which corresponds to the largest peak, canbe interpreted as the most likely geographic region intended by a personusing the phrase “capitol hill.”

FIG. 7B is a diagram illustrating another exemplary histogram, histogram710, for the bi-gram “bay area.” Histogram 710 includes two peaks, asmaller one centered around the Green Bay, Wis., area, and a larger peakdefining the San Francisco, Calif., bay area.

Location classifier 100 may perform act 407 for some or all of theterms/phrases occurring in text selections 610. In one implementation,location classifier 100 may generate a histogram for all the bi-grams(two word phrases) that occur in text 610. In other implementations,histograms may also be generated for longer phrases or single terms.

Certain occurrences of terms/phrases may be ignored when accumulatingoccurrences of terms/phrases. Some boilerplate language may occurfrequently in a set of training documents, although the boilerplatelanguage is not necessarily relevant for determining geographicalrelevance. Accordingly, in some implementations, terms to left and/orright of a select term/phrase may also be examined, and the term/phraseaccumulated only when these terms are different than previous instancesof the terms to the left or right of the term/phrase. Thus, if aterm/phrase does not occur in a legitimate new context, it may beignored.

Location classifier 100 may next select and store the generatedhistograms that correspond to geographically relevant terms/phrases(acts 408 and 409). The stored histograms act as geo-relevance profilesfor the terms/phrases. Many of the terms/phrases for which histogramsare generated in act 407 may not be geographically relevant. FIG. 7C isa diagram of an exemplary histogram 720 for the for the bi-gram “livebookmarks.” This phrase is not geographically relevant, and accordingly,the histogram is relatively flat. Histograms 700 and 710, however,include statistically significant spikes that indicate that theseterms/phrases may be relevant to a particular geographic location. Oneof ordinary skill in the art will recognize that a number of knowntechniques could be used to determine whether a histogram includesstatistically significant peaks.

Acts 408 and 409 may be repeated for a number of terms/phrases in textselections 610 (act 410). In one implementation, location classifier 100may examine the geographical relevance of every bi-gram present in textselections 610. In other implementations, single terms could be examinedfor geographical relevance or phrases having three or more terms couldbe examined.

As a result of the training shown in FIG. 4, location classifier 100 maystore a number (potentially a large number) of terms/phrases and theircorresponding geo-relevance profiles. FIG. 8 is a diagram conceptuallyillustrating a table 800 including exemplary terms/phrases and theircorresponding geo-relevance profiles.

In one implementation, the geo-relevance profiles stored in act 409 maybe normalized based on the global distribution of zip codes in thetraining data. In this manner, regions that are frequently mentioned inthe training data are not over emphasized in the geo-relevance profiles.

Operation of Location Classifier 100

FIG. 9 is a flow chart illustrating exemplary operation of locationclassifier 100 in determining potentially relevant geographical areasfor input documents.

Location classifier 100 may begin by receiving the input document (act901). Generally, the input document will be one that includespotentially ambiguous references to locations. The input document may,for example, be a relatively short section of text, such as a searchquery, or a longer block of text such as a web document. Terms/phrasesmay be located in the input document that correspond to theterms/phrases stored in table 800 (act 902). In other words, theterms/phrases that were previously determined to have geographicalrelevance are identified.

The geo-relevance profiles for each of the identified terms/phrases maynext be combined to generate a resultant geo-relevance profile for theinput document (act 903). In one implementation, the geo-relevanceprofiles may be combined by multiplying each of the geo-relevanceprofiles identified in act 902. That is, for each zip code, the valuesfor each histogram may be multiplied together to obtain a value for thatzip code in the resultant histogram. FIGS. 10A-10C illustrate combiningmultiple geo-relevance profiles to obtain a combined profile. In thisexample, assume that the input document is a page of text that containstwo bi-grams that are present in table 800 (i.e., the input pagecontains two geographically relevant terms/phrases). The two bi-gramsare “Castro Street” and “Bay Area.” The geo-relevance profile for CastroStreet is shown in FIG. 10A and the geo-relevance profile for Bay Areais shown in FIG. 10B. FIG. 10C illustrates the combined geo-relevanceprofile. As shown, although the histograms in FIGS. 10A and 10B bothinclude multiple peaks, when combined, the peaks tend to cancel eachother except in areas where both profiles indicate geographicalrelevance. Accordingly, the combined geo-relevance profile of FIG. 10Ccorrectly indicates that the reference to “Castro Street” and “Bay Area”is most likely a reference to the Castro Street located in the NorthernCalifornia Bay Area.

Based on the combined geo-relevance profile, such as the exemplaryprofile shown in FIG. 10C, location classifier 100 may generate outputinformation defining potential relevance of the input documents to oneor more geographical regions (act 904). The output information maygenerally be obtained by examining the combined geo-relevance profilefor peaks. In the example of FIG. 10C, for instance, the outputinformation may include zip codes of regions that include Castro Streetin Northern California. In some implementations, the zip codes may alsobe associated with values that relate the likeliness or certainty thatthe area defined by the zip code is correct.

In one implementation, the document received in act 901 may be a partialaddress, such as a partial address taken from a web page, search query,or other source. The output information may then be used to disambiguatethe partial address. For instance, if an address such as “650 CastroStreet” is identified in a document without a city or state, the addressby itself is not a complete address. If, however, location classifier100 concludes that the document is relevant to the Mountain View zipcode 94043, then the address is unambiguous and can be reduced to anexact geographical location (latitude/longitude).

Exemplary Implementation

FIG. 11 is a diagram illustrating an exemplary implementation oflocation classifier 100 implemented in the context of a search engine. Anumber of users 1105 may connect to a search engine 1110 over a network1115, such as the Internet. Search engine 1110 may be a local searchengine that returns links to a ranked set of documents, from a database1120, that are related to a user query that the user intends to apply toa certain geographical region.

Location classifier 100 may assist search engine 1110 in determining thegeographical relevance (if any) of the documents in database 1120. Inparticular, location classifier 100 may geographically classify each ofthe documents, or portions of the documents, that cannot be otherwisepositively identified as being associated with a particular geographicarea. This geographic classification information may then be stored indatabase 1120 as location identifiers with their corresponding documentsthat search engine 1110 may use in responding to user search queries.

In another possible exemplary implementation, location classifier 100may operate on the search queries received from users 1105. Locationclassifier 100 may thus provide geographical relevance informationpertaining to a search query. This information may be used to assistsearch engine 1110 in returning relevant results to the user.

CONCLUSION

As described above, a location classifier generates location informationbased on terms/phrases in input text. The terms/phrases can includeterms/phrases that would normally be considered geographicallyambiguous.

It will be apparent to one of ordinary skill in the art that aspects ofthe invention, as described above, may be implemented in many differentforms of software, firmware, and hardware in the implementationsillustrated in the figures. The actual software code or specializedcontrol hardware used to implement aspects consistent with the presentinvention is not limiting of the present invention. Thus, the operationand behavior of the aspects were described without reference to thespecific software code—it being understood that a person of ordinaryskill in the art would be able to design software and control hardwareto implement the aspects based on the description herein.

The foregoing description of preferred embodiments of the presentinvention provides illustration and description, but is not intended tobe exhaustive or to limit the invention to the precise form disclosed.Modifications and variations are possible in light of the aboveteachings or may be acquired from practice of the invention. Forexample, although many of the operations described above were describedin a particular order, many of the operations are amenable to beingperformed simultaneously or in different orders. Additionally, althoughthe location classifier was generally described as being part of asearch engine, it should be understood that the search engine may moregenerally be separate from the location classifier.

No element, act, or instruction used in the present application shouldbe construed as critical or essential to the invention unless explicitlydescribed as such. Also, as used herein, the article “a” is intended topotentially allow for one or more items. Where only one item isintended, the term “one” or similar language is used. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise. The scope of the invention isdefined by the claims and their equivalents.

1. A method of determining a geographical relevance of a document,performed by one or more processors associated with one or more serverdevices, the method comprising: generating, using a processor associatedwith the one or more server devices, a plurality of histogramsassociated with a plurality of respective strings in the document bycomparing strings in the document to a pre-existing plurality of stringsfor which histograms were previously generated, where the plurality ofhistograms relates occurrences of strings to geographical regions;generating, using a processor associated with the one or more serverdevices, a combined histogram for the document from the plurality ofhistograms by multiplying together values for each histogram of theplurality of histograms; and identifying, using a processor associatedwith the one or more server devices, a geographical relevance of thedocument based on the combined histogram.
 2. The method of claim 1,where identifying the geographical relevance of the document based onthe combined histogram includes: analyzing the combined histogram forpeaks.
 3. The method of claim 1, where one or more strings in theplurality of strings comprise a single term string.
 4. The method ofclaim 1, where one or more strings in the plurality of strings comprisebi-grams.
 5. The method of claim 1, where the document includes a webpage.
 6. The method of claim 1, where the document includes a searchquery.
 7. A location classifier implemented within a computing device,comprising: means for receiving input text; a memory to store the inputtext; means for locating strings within the input text that werepreviously determined to be geographically relevant; means for comparinglocated strings in the input text to a pre-existing plurality of stringsfor which histograms were previously generated, where the histogramsrelate occurrences of strings to geographical regions; means forcombining the retrieved histograms, to form a combined histogram, bymultiplying together values for each histogram of the retrievedhistograms; and means for determining whether the input text isgeographically relevant based on the peaks of the combined histogram. 8.The location classifier of claim 7, where the plurality of histogramsrelate zip codes to a number of occurrences of a string in documentsassociated with a particular zip code.
 9. A memory device containingprogramming instructions for execution by a processor, the memory devicecomprising: programming instructions for generating a plurality ofhistograms associated with a respective plurality of strings in adocument by examining the document to locate strings for the respectiveplurality of strings by comparing strings in the document to apre-existing plurality of strings for which histograms were previouslygenerated, the histograms defining a geographical relevance of theplurality of strings with respect to geographical regions; programminginstructions for combining the plurality of histograms to obtain acombined histogram for the document by multiplying together values foreach histogram of the plurality of histograms; and programminginstructions for determining a geographical relevance of the documentbased on the combined histogram.
 10. The memory device of claim 9, wherethe programming instructions for a determining geographical relevance ofthe document further includes: programming instructions for analyzingthe combined histogram for peaks.
 11. The memory device of claim 9,where the strings include single term strings.
 12. The memory device ofclaim 9, where the strings include bi-grams.
 13. The memory device ofclaim 9, where the document includes a web page.
 14. The memory deviceof claim 9, where the document includes a search query.
 15. A method forgenerating a histogram for a string, performed by a server device, themethod comprising: determining, using a processor of the server device,a plurality of sections of training text in which each section oftraining text is associated with a geographical region; accumulating,using the processor, occurrences of the string in the plurality ofsections of training text when terms to a left or right of the stringare different from previous instances of terms to the left or right ofthe string; and generating, using the processor, the histogram based onthe accumulated occurrences of the string, where the histogram relatesthe occurrences of the string to geographical regions.
 16. The method ofclaim 15, where determining a plurality of sections of training textincludes: locating known geographic signals in a plurality of documents;and selecting the sections of training text from those of the pluralityof documents that include the known geographic signals.
 17. The methodof claim 15, where the documents include web documents.
 18. The methodof claim 15, further comprising: normalizing the generated histogrambased on a global distribution of geographical regions within thetraining text.
 19. The method of claim 15, where the geographicalregions are represented by zip codes.
 20. The method of claim 15,further comprising: saving the generated histogram for future use whenthe histogram includes statistically relevant peaks.
 21. A devicecomprising: a processor; and a computer-readable memory coupled to theprocessor and containing instructions that when executed by theprocessor cause the processor to: determine a plurality of sections oftraining text in which each section of training text is associated witha geographical region; accumulate occurrences of a string in theplurality of sections of training text when terms to a left or right ofthe string are different from previous instances of the terms to theleft or right of the string; and generate a histogram that defines thegeographical relevance of the string with respect to geographicalregions based on the accumulated occurrences of the string.
 22. Thedevice of claim 21, further comprising analyzing the histogram forpeaks.
 23. The device of claim 21, where the string comprises a singleterm.
 24. The device of claim 21, where the string comprises a bi-gram.25. A method performed by one or more processors associated with one ormore server devices, the method comprising: examining, using a processorassociated with the one or more server devices, a document to locatestrings that were previously determined to have geographical relevance;generating, using a processor associated with the one or more serverdevices, a plurality of histograms associated with the located strings,in the document, by comparing the located strings in the document to apre-existing plurality of strings for which histograms were previouslygenerated; receiving, using a communication interface or an input deviceof the one or more server devices, the histograms associated with thestrings, where the histograms relate occurrences of the strings togeographic regions; obtaining, using a processor associated with the oneor more server devices, a combined histogram for the document from thereceived histograms by multiplying values for each of the receivedhistograms together; analyzing, using a processor associated with theone or more server devices, the combined histogram for peaks; anddetermining, using a processor associated with the one or more serverdevices, geographical relevance of the document based on whether peaksare present in the combined histogram.
 26. The method of claim 25,further comprising normalizing the received histograms based on a globaldistribution of geographical regions within the training text.
 27. Themethod of claim 25, where the strings comprise one of a single term or abi-gram.
 28. The method of claim 25, where the histograms relate zipcodes to a number of occurrences of a string in documents associatedwith a particular zip code.